Homework 7: Mapping 500 Gas Stations and Philidelphia Area Crimes to Demonstrate Knowledge of Plotting on a Map

1 Bringing in and Modifying the dataset

To work with the Countries dataset, we will be bringing in Country Info from my github. We will also be limiting it to 2015 for our plot of life expectancy and income in 2015.

POC<-read.csv(file="https://dannyoc99.github.io/DOCAL/dataset/POC.csv")
POC500<-sample_n(POC,500)
pocprev<-POC500[7:9]

2 A look into the dataset

DT::datatable(pocprev, fillContainer = FALSE, options = list(pageLength = 11))

3 The 500 Gas Stations Chosen

g <- list(      scope = 'usa',
           projection = list(type = 'albers usa'),
             showland = TRUE,
            landcolor = toRGB("gray95"),
         subunitcolor = toRGB("gray85"),
         countrycolor = toRGB("gray85"),
         countrywidth = 0.5,
         subunitwidth = 0.5
       )
###
fig <- plot_geo(POC500, lat = ~ycoord, lon = ~xcoord) %>% 
  add_markers( text = ~paste(STATE, "<br>",
                           "County:", county, "<br>",
                           "Address", ADDRESS, "<br>",
                           "City:", CITY, "<br>",
                           "Zip:", ZIPnew),
              color = ~truckstop_flag, 
              symbol = "circle", 
              hoverinfo = "text")   %>% 
  layout( title = '500 Gas Stations in the United States', 
          geo = g,
          legend = list(title = list(text = "<b>Truckstop Y/N</b>"))) 

fig

Within our data, we wanted to take a look at 500 random gas stations within the united States. Within our sample, a large majority of our sample was on the east coast, west coast, and Texas. This makes a lot of sense, because when you consider the population densities of the United States, these would have the highest densities, and thus would require more gas stations. To have the graph give more information, I included whether or not a gas station is also a truck stop as a color. Looking at this information, we see that many of the midwest gas stations are truck stops, but most of the coastal states are just gas stations. This also makes sense as many of the mid western states do not have a ton of people living in them, so they are primarily used for travel requiring the gas stations to be truck stops.

4 Preparing Crime Statistics Data

crime_raw<-POC<-read.csv(file="https://dannyoc99.github.io/DOCAL/dataset/PhillyCrimeSince2015.csv")
crime2023<-mutate(crime_raw,year=format(as.Date(date,format="%m/%d/%Y"),"%Y"))%>%filter(year==2023)

5 Mapping Philidelphia 2015 Crimes

g <- list(      scope = 'usa',
           projection = list(type = 'albers usa'),
             showland = TRUE,
            landcolor = toRGB("gray95"),
         subunitcolor = toRGB("gray85"),
         countrycolor = toRGB("gray85"),
         countrywidth = 0.5,
         subunitwidth = 0.5,
         scale=.3,
         center_lat=-75,
         center_long=40
       )

fig <- plot_geo(crime2023, lat = ~lat, lon = ~lng) %>% 
  add_markers( text = ~paste("Fatal:",fatal, "<br>",
                            "Street:",street_name, "<br>",
                           "Zip:", zip_code, "<br>",
                           "Neighborhood", neighborhood, "<br>",
                           "Sex:", sex, "<br>",
                           "Race:", race),
              color = ~fatal, 
              symbol = "circle", 
              hoverinfo = "text")   %>% 
  layout( title = '2023 Philidelphia Area Crimes', 
          geo = g,
          legend = list(title = list(text = "<b>Fatality Y/N</b>")),
          xaxis = list(range = list(-77, -74)),
yaxis = list(range = list(44, 46))) 

fig